Improved eigenspace-based method for robust adaptive beamforming with dimension search

Abstract

The eigenspace-based technique, as a classic method for robust adaptive beamforming (RAB), can substantially mitigate the negative impact caused by model mismatch. However, the conventional eigenspace-based beamformer (CESB) undergoes severe performance degradation at low signal-to-noise ratio (SNR). This is because it firmly sets the dimension of the projection subspace (DOPS) to the source number. To alleviate this problem, an improved eigenspace-based approach is proposed in this communication, where the DOPS is auto-selected to adapt to different cases. We roughly analyze the rationality that the robustness of the eigenspace-based technique can be enhanced by simply adjusting the DOPS at low SNR. In addition, we concretely propose an automatic selection method for the DOPS derived from a minimum beamformer sensitivity estimation problem. The proposed method is advantageous in that it is parameter-free. Moreover, simulation experiments are performed to demonstrate the improvement of the CESB and its robustness against steering vector mismatch.